AI Data Scientist

About FireSafe AI:FireSafe AI is a leading innovator in AI-powered wildfire detection and risk mitigation technologies. We help communities, governments, and businesses protect lives and assets by providing real-time wildfire analytics, early warning systems, and actionable insights through our advanced AI solutions.Position Overview:We’re working on an IRAP-funded R&D project focused on building deep neural networks (DNNs) for wildfire spread simulation on an web/cloud platform.As an AI Data Researcher, you will lead the design, experimentation, and deployment of deep learning models for wildfire prediction.You’ll work end-to-end: from data preparation and feature engineering, to model design and evaluation, to integration.Key Responsibilities:Develop and refine time-series prediction models (e.g. RNNs, LSTMs, Transformers) using:Historical and real-time environmental data (weather, vegetation/fuel, terrain, etc.)Records of past wildfire or related eventsOwn the data science workflow:Data collection, cleaning, and feature engineering for temporal and spatial datasetsDesigning experiments, running training jobs, and analyzing resultsHelp build a simulation-oriented ML pipeline that:Ingests environmental dataProduces predictions or risk scores related to wildfire behaviour and spreadIntegrates with our existing web/cloud servicesCollaborate with a small team of engineers and domain experts to:Translate real-world constraints (fire behaviour, operations, etc.) into model requirementsIterate quickly on ideas and make pragmatic choices to hit milestones by March 31Qualifications:Eligible to work in Canada on a T4 contract for the full project durationStrong hands-on experience with deep learning for time-series or sequential data, ideally in Python with PyTorch or TensorFlow/KerasSolid skills in:Data wrangling and feature engineering (Pandas, NumPy, etc.)Training, validating, and comparing ML models on real, noisy dataPractical experience on AWS with at least some of:S3, EC2, ECS/EKS, Lambda, or similar compute / storage servicesAny ML deployment experience (SageMaker, custom Dockerized services, etc.)Comfortable working in a small, fast-moving team, communicating clearly, and taking ownership from prototype to productionNice to HaveExperience with environmental modeling, climate / weather data, remote sensing, or wildfire riskFamiliarity with geospatial data and tools (e.g. raster/vector data, GDAL, GeoPandas)MLOps / experiment tracking tools (MLflow, Weights & Biases, SageMaker Experiments, etc.)Building APIs and services (REST, gRPC, or GraphQL)Previous work on research-style or government-funded projects is a plus (IRAP, NSERC, etc.)How to ApplySend us:Your CV or LinkedIn profileA short note on:Relevant time-series / deep learning projectsAny environmental / geospatial / wildfire experienceYour AWS experience in practice (what you used and for what)(Optional) Links to GitHub, publications, or portfolioto: careers@firesafe.live